Data classification is using an multi-class annotation tool embedded on t6 IoT. The concept is to assign a category to any Datapoints based on Rules.
Benefits on setting t6 for your Data Annotation & Classification
Using t6 Decision Rule maximize and ease the annotation process. This implies :
- a fast process on annotating datapoints ;
- an automatic identification based on Rules without any human requirement (after setting up the rules) ;
- a reliable and robust identification, whatever the number of datapoint, this annotation will defined the correct Classification from your rules.
t6 data annotation
t6 preprocessor is flexible and allow any of the following classification types:
Binary Classification
The Classification is having only 2 possible options (2 classes). This can be used on identifying email spam detection (the Classification is either “spam” or “not spam”, and can’t be both at the same time, or any other variation).
Multi-Class Classification
A Multi-Class Classification on the opposite is allowing to have more than 2 classes in the Classification. For instance it can be used to identify either the datapoint is “Walking”, “Running”, “Flying” OR “Swimming”.
Multi-Annotation (or Multi-Label) Classification
This third type is the more flexible and allows the datapoint to relate to multi Binary or Multi-Class Classifications. Hence, it combine multiple Classes together.
Automate classification on measurements
Identify classes
This step is crucial as it will define the whole classification on your measurement. Identifying classes require a full knowledge of the business behind data and measurements. Thus became business focus. That is the reason why we have set t6 to trust human and business owners on this task.
Define criteria - rules
Decision Rule requires basic criteria combination to fully handle the automation. As t6 is Multi-Annotation Classification, having multiple Rules on the same Measured datapoint is feasible. Hence, we can easily and automatically setup several annotations to the same measured just by using multiple Rules distinctively.
Setup a Decision Rule that will trigger the annotation
The idea behind those trigger is very simple :
- first you’d need to have a Flow that will contains the Datapoints measured as Facts;
- and a Category to be used in the Classification;
Then, the combination of the criteria is defined by Conditions matching operators from Decision Rule. And finally, the Action trigger should be “annotate” including an additional category_id on its event parameters.
Hence, each time the datapoints will match the condition(s), then annotation process will take other and attribute the Category according to the Rule parameter. And voila.
More details about t6 IoT
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